Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

Abstract

Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

Prediction of Surface Roughness of Monel k 500 Super Alloy by Using Artificial Neural Network

“…According to Table 2, the average percentage surface roughness (Ra) error prediction values is 1.845% the obtained over all prediction error percentage is less when compare with the previous work [4]. The neuron in the input layer correlates to the cutting path's point angle, feed rate, and depth of cut.…”

“…An absolute percentage error (APE) of less than 6%, a mean squared error (MSE) of less than 0.3%, and an R2 value of 99% all prove that the ANN structure is appropriate. [4].…”

A review on conventional and nonconventional machining of Nickel-based Nimonic superalloy

“…FR was observed as the most significant factor in minimizing the cutting force and flank wear, followed by CS and DOC. Kumar et al [65] used a wavelet-based histogram equalization technique to model the SR in the turning of Nimonic C263. The proposed noncontact type SR evaluation method was found to be effective, with a mean prediction error of 3.16%.…”

Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques

“…Machined surface texture identification is of the utmost importance in production firms, as it helps to measure the roughness of the surface. Various methods have been used for surface texture identification by earlier researchers [11][12][13]. However, the Tamura features are considered one of the essential features in identifying patterns, which is not studied in determining surface texture [8,14].…”